OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Closed Loop OFDM-Based Resource Allocation Method using Machine Learning and Genetic Algorithm
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, the concept of Machine Learning (ML) is introduced to the Orthogonal Frequency Division Multiple Access-based (OFDMA-based) scheduler. Similar to the impact of the Channel Quality Indicator (CQI) on the scheduler in the Long Term Evolution (LTE), ML is utilized to provide the scheduler with pertinent information about the User Equipment (UE) traffic patterns, demands, Quality of Service (QoS) requirements, instantaneous user throughput and other network conditions. An adaptive ML-based framework is proposed in order to optimize the LTE scheduler operation. The proposed technique targets multiple objective scheduling strategies. The weights of the different objectives are adjusted to optimize the resources allocation per transmission based on the UEs demand pattern. In addition, it overcomes the trade-off problem of the traditional scheduling methods. The technique can be used as a generic framework with any scheduling strategy. In this paper, Genetic Algorithm-based (GA-based) multi- objective scheduler is considered to illustrate the efficiency of the proposed adaptive scheduling solution. Results show that using the combination of clustering and classification algorithms along with the GA optimizes the GA scheduler functionality and makes use of the ML process to form a closed loop scheduling mechanism.
Realistic DNA De-anonymization using Phenotypic Prediction
Venue: arXiv cs.CR 2016
Year: 2016

There are a number of vectors for attack when trying to link an individual to a certain DNA sequence. Phenotypic prediction is one such vector; linking DNA to an individual based on their traits. Current approaches are not overly effective, due to a number of real world considerations. This report will improve upon current phenotypic prediction, and suggest a number of methods for defending against such an attack.
Offline Delay-Optimal Transmission for Energy Harvesting Nodes
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates the offline packet-delay-minimization problem for an energy harvesting transmitter. To overcome the non-convexity of the problem, we propose a C2-diffeomorphic transformation and provide the necessary and sufficient condition for the transformed problem to a standard convex optimization problem. Based on this condition, a simple choice of the transformation is determined which allows an analytically tractable solution of the original non-convex problem to be easily obtained once the transformed convex problem is solved. We further study the structure of the optimal transmission policy in a special case and find it to follow a weighted-directional-water-filling structure. In particular, the optimal policy tends to allocate more power in earlier time slots and less power in later time slots. Our analytical insight is verified by simulation results.
Secret Key Generation Through a Relay
Venue: arXiv cs.IT 2016
Year: 2016

We consider problems of two-user secret key generation through an intermediate relay. Each user observes correlated source sequences and communicates to the relay over rate-limited noiseless links. The relay processes and broadcasts information to the two users over another rate-limited link. The users then generate a common secret key based on their sources and information from the relay. In the untrusted relay setting, the goal is to establish key agreement between the two users at the highest key rate without leaking information about the key to the relay. We characterize inner and outer bounds to the optimal tradeoff between communication and key rates. The inner bound is based on the scheme which involves a combination of binning, network coding, and key aggregation techniques. For the trusted relay setting with a public broadcast link, the optimal communication-key rate tradeoff is provided for a special case where the two sources are available losslessly at the relay. The results can be relevant for cloud-based secret key generation services.
Parallelized Proximity-Based Query Processing Methods for Road Networks
Venue: arXiv cs.DC 2016
Year: 2016

In this paper, we propose a paradigm for processing in parallel graph joins in road networks. The methodology we present can be used for distance join processing among the elements of two disjoint sets R,S of nodes from the road network, with R preceding S, and we are in search for the pairs of vertices (u,v), where u in R and v in S, such that dist(u,v) < θ. Another variation of the problem would involve retrieving the k closest pairs (u,v) in the road network with u in R and v in S, such that dist(u,v) <= dist(w,y), where w,y do not belong in the result. We reckon that this is an extremely useful paradigm with many practical applications. A typical example of usage of our methods would be to find the pairs of restaurants and bars (in that order) from which to select for a night out, that either fall within walking distance for example, or just the k closest pairs, depending on the parameters. Another entirely different scenario would involve finding the points of two distinct trajectories that are within a certain distance predicate, or the k closest such points. For example, we would like to transfer from one train to another a few tones of freight, and hence, we want to minimize the distance we have to cover for moving the cargo from the carrying train to the other. We reckon that this endeavor of ours covers exactly those needs for processing such queries efficiently. Moreover, for the specific purposes of this paper, we also propose a novel heuristic graph partitioning scheme. It resembles a recursive bisection method, and is tailored to the requirements of the problem, targeting at establishing well separated partitions, so as to allow computations to be performed simultaneously and independently within each partition, unlike hitherto work that aims at minimizing either the number of edges among different partitions, or the number of nodes thereof.
Leveraging Unstructured Data to Detect Emerging Reliability Issues
Venue: arXiv cs.AI 2016
Year: 2016

Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, which generally describe symptoms, and technician comments, which outline diagnostics and treatment information. A legitimate customer complaint can eventually be tracked to a failure or a claim. However, there is a delay between the time of a customer complaint and the time of a failure or a claim. A proactive strategy aimed at analyzing customer complaints for symptoms can help service providers detect reliability problems in advance and initiate corrective actions such as recalls. This paper introduces essential text mining concepts in the context of reliability analysis and a method to detect emerging reliability issues. The application of the method is illustrated using a case study.
From Graph Isoperimetric Inequality to Network Connectivity -- A New Approach
Venue: arXiv cs.DC 2016
Year: 2016

We present a new, novel approach to obtaining a network's connectivity. More specifically, we show that there exists a relationship between a network's graph isoperimetric properties and its conditional connectivity. A network's connectivity is the minimum number of nodes, whose removal will cause the network disconnected. It is a basic and important measure for the network's reliability, hence its overall robustness. Several conditional connectivities have been proposed in the past for the purpose of accurately reflecting various realistic network situations, with extra connectivity being one such conditional connectivity. In this paper, we will use isoperimetric properties of the hypercube network to obtain its extra connectivity. The result of the paper for the first time establishes a relationship between the age-old isoperimetric problem and network connectivity.
Uber: Utilizing Buffers to Simplify NoCs for Hundreds-Cores
Venue: arXiv cs.AR 2016
Year: 2016

Approaching ideal wire latency using a network-on-chip (NoC) is an important practical problem for many-core systems, particularly hundreds-cores. Although other researchers have focused on optimizing large meshes, bypassing or speculating router pipelines, or creating more intricate logarithmic topologies, this paper proposes a balanced combination that trades queue buffers for simplicity. Preliminary analysis of nine benchmarks from PARSEC and SPLASH using execution-driven simulation shows that utilization rises from 2% when connecting a single core per mesh port to at least 50%, as slack for delay in concentrator and router queues is around 6x higher compared to the ideal latency of just 20 cycles. That is, a 16-port mesh suffices because queueing is the uncommon case for system performance. In this way, the mesh hop count is bounded to three, as load becomes uniform via extended concentration, and ideal latency is approached using conventional four-stage pipelines for the mesh routers together with minor logarithmic edges. A realistic Uber is also detailed, featuring the same performance as a 64-port mesh that employs optimized router pipelines, improving the baseline by 12%. Ongoing work develops techniques to better balance load by tuning the placement of cache blocks, and compares Uber with bufferless routing.
ABROA : Audio-Based Room-Occupancy Analysis using Gaussian Mixtures and Hidden Markov Models
Venue: arXiv cs.SD 2016
Year: 2016

This paper outlines preliminary steps towards the development of an audio- based room-occupancy analysis model. Our approach borrows from speech recognition tradition and is based on Gaussian Mixtures and Hidden Markov Models. We analyze possible challenges encountered in the development of such a model, and offer several solutions including feature design and prediction strategies. We provide results obtained from experiments with audio data from a retail store in Palo Alto, California. Model assessment is done via leave-two-out Bootstrap and model convergence achieves good accuracy, thus representing a contribution to multimodal people counting algorithms.
Multi-Variant Execution of Parallel Programs
Venue: arXiv cs.CR 2016
Year: 2016

Multi-Variant Execution Environments (MVEEs) are a promising technique to protect software against memory corruption attacks. They transparently execute multiple, diversified variants (often referred to as replicae) of the software receiving the same inputs. By enforcing and monitoring the lock-step execution of the replicae's system calls, and by deploying diversity techniques that prevent an attacker from simultaneously compromising multiple replicae, MVEEs can block attacks before they succeed. Existing MVEEs cannot handle non-trivial multi-threaded programs because their undeterministic behavior introduces benign system call inconsistencies in the replicae, which trigger false positive detections and deadlocks in the MVEEs. This paper for the first time extends the generality of MVEEs to protect multi-threaded software by means of secure and efficient synchronization replication agents. On the PARSEC 2.1 parallel benchmarks running with four worker threads, our prototype MVEE incurs a run-time overhead of only 1.32x.
A New Approach to SMS Steganography using Mathematical Equations
Venue: arXiv cs.CR 2016
Year: 2016

In the era of Information Technology, cyber-crime has always been a worrying issue for online users. Phishing, social engineering, and third party attacks have made people reluctant to share their personal information, even with trusted entities. Messages that are sent via Short Message Service (SMS) are easily copied and hacked by using special software. To enforce the security of sending messages through mobile phones, one solution is SMS steganography. SMS Steganography is a technique that hides a secret message in the SMS. We propose a new approach for SMS steganography that uses a mathematical equation as the stego media in order to transmit the data. With this approach, we can hide up to 35 characters (25%) of a secret message on a single SMS with maximum of 140 characters.
Learning of Generalized Low-Rank Models: A Greedy Approach
Venue: arXiv cs.LG 2016
Year: 2016

Learning of low-rank matrices is fundamental to many machine learning applications. A state-of-the-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used in matrix completion problems with the square loss. In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or strongly convex. The proposed algorithm has low per-iteration time complexity and fast convergence rate. Experimental results show that it is much faster than the state-of-the-art, with comparable or even better prediction performance.
Mutual Information Optimally Local Private Discrete Distribution Estimation
Venue: arXiv cs.IT 2016
Year: 2016

Consider statistical learning (e.g. discrete distribution estimation) with local $ε$-differential privacy, which preserves each data provider's privacy locally, we aim to optimize statistical data utility under the privacy constraints. Specifically, we study maximizing mutual information between a provider's data and its private view, and give the exact mutual information bound along with an attainable mechanism: $k$-subset mechanism as results. The mutual information optimal mechanism randomly outputs a size $k$ subset of the original data domain with delicate probability assignment, where $k$ varies with the privacy level $ε$ and the data domain size $d$. After analysing the limitations of existing local private mechanisms from mutual information perspective, we propose an efficient implementation of the $k$-subset mechanism for discrete distribution estimation, and show its optimality guarantees over existing approaches.
Satisfiability Checking meets Symbolic Computation (Project Paper)
Venue: arXiv cs.SC 2016
Year: 2016

Symbolic Computation and Satisfiability Checking are two research areas, both having their individual scientific focus but sharing also common interests in the development, implementation and application of decision procedures for arithmetic theories. Despite their commonalities, the two communities are rather weakly connected. The aim of our newly accepted SC-square project (H2020-FETOPEN-CSA) is to strengthen the connection between these communities by creating common platforms, initiating interaction and exchange, identifying common challenges, and developing a common roadmap from theory along the way to tools and (industrial) applications. In this paper we report on the aims and on the first activities of this project, and formalise some relevant challenges for the unified SC-square community.
Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies
Venue: arXiv cs.AI 2016
Year: 2016

In the medical domain, the continuous stream of scientific research contains contradictory results supported by arguments and counter-arguments. As medical expertise occurs at different levels, part of the human agents have difficulties to face the huge amount of studies, but also to understand the reasons and pieces of evidences claimed by the proponents and the opponents of the debated topic. To better understand the supporting arguments for new findings related to current state of the art in the medical domain we need tools able to identify arguments in scientific papers. Our work here aims to fill the above technological gap. Quite aware of the difficulty of this task, we embark to this road by relying on the well-known interleaving of domain knowledge with natural language processing. To formalise the existing medical knowledge, we rely on ontologies. To structure the argumentation model we use also the expressivity and reasoning capabilities of Description Logics. To perform argumentation mining we formalise various linguistic patterns in a rule-based language. We tested our solution against a corpus of scientific papers related to breast cancer. The run experiments show a F-measure between 0.71 and 0.86 for identifying conclusions of an argument and between 0.65 and 0.86 for identifying premises of an argument.
Statistical Delay Bound for WirelessHART Networks
Venue: arXiv cs.PF 2016
Year: 2016

In this paper we provide a performance analysis framework for wireless industrial networks by deriving a service curve and a bound on the delay violation probability. For this purpose we use the (min,x) stochastic network calculus as well as a recently presented recursive formula for an end-to-end delay bound of wireless heterogeneous networks. The derived results are mapped to WirelessHART networks used in process automation and were validated via simulations. In addition to WirelessHART, our results can be applied to any wireless network whose physical layer conforms the IEEE 802.15.4 standard, while its MAC protocol incorporates TDMA and channel hopping, like e.g. ISA100.11a or TSCH-based networks. The provided delay analysis is especially useful during the network design phase, offering further research potential towards optimal routing and power management in QoS-constrained wireless industrial networks.
Adaptive foveated single-pixel imaging with dynamic super-sampling
Venue: arXiv cs.CV 2016
Year: 2016

As an alternative to conventional multi-pixel cameras, single-pixel cameras enable images to be recorded using a single detector that measures the correlations between the scene and a set of patterns. However, to fully sample a scene in this way requires at least the same number of correlation measurements as there are pixels in the reconstructed image. Therefore single-pixel imaging systems typically exhibit low frame-rates. To mitigate this, a range of compressive sensing techniques have been developed which rely on a priori knowledge of the scene to reconstruct images from an under-sampled set of measurements. In this work we take a different approach and adopt a strategy inspired by the foveated vision systems found in the animal kingdom - a framework that exploits the spatio-temporal redundancy present in many dynamic scenes. In our single-pixel imaging system a high-resolution foveal region follows motion within the scene, but unlike a simple zoom, every frame delivers new spatial information from across the entire field-of-view. Using this approach we demonstrate a four-fold reduction in the time taken to record the detail of rapidly evolving features, whilst simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This tiered super-sampling technique enables the reconstruction of video streams in which both the resolution and the effective exposure-time spatially vary and adapt dynamically in response to the evolution of the scene. The methods described here can complement existing compressive sensing approaches and may be applied to enhance a variety of computational imagers that rely on sequential correlation measurements.
VHT: Vertical Hoeffding Tree
Venue: arXiv cs.DC 2016
Year: 2016

IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and visualize. In the literature, we can find distributed algorithms for learning decision trees, and also streaming algorithms, but not algorithms that combine both features. In this paper we present the Vertical Hoeffding Tree (VHT), the first distributed streaming algorithm for learning decision trees. It features a novel way of distributing decision trees via vertical parallelism. The algorithm is implemented on top of Apache SAMOA, a platform for mining distributed data streams, and thus able to run on real-world clusters. We run several experiments to study the accuracy and throughput performance of our new VHT algorithm, as well as its ability to scale while keeping its superior performance with respect to non-distributed decision trees.
A New Lightweight Algorithm to compute the BWT and the LCP array of a Set of Strings
Venue: arXiv cs.DS 2016
Year: 2016

Indexing of very large collections of strings such as those produced by the widespread sequencing technologies, heavily relies on multi-string generalizations of the Burrows-Wheeler Transform (BWT), and for this problem various in-memory algorithms have been proposed. The rapid growing of data that are processed routinely, such as in bioinformatics, requires a large amount of main memory, and this fact has motivated the development of algorithms, to compute the BWT, that work almost entirely in external memory. On the other hand, the related problem of computing the Longest Common Prefix (LCP) array is often instrumental in several algorithms on collection of strings, such as those that compute the suffix-prefix overlap among strings, which is an essential step for many genome assembly algorithms. The best current lightweight approach to compute BWT and LCP array on a set of $m$ strings, each one $k$ characters long, has I/O complexity that is $O(mk^2 \log |Σ|)$ (where $|Σ|$ is the size of the alphabet), thus it is not optimal. In this paper we propose a novel approach to build BWT and LCP array (simultaneously) with $O(kmL(\log k +\log σ))$ I/O complexity, where $L$ is the length of longest substring that appears at least twice in the input strings.
An Optimized Huffmans Coding by the method of Grouping
Venue: arXiv cs.IT 2016
Year: 2016

Data compression has become a necessity not only the in the field of communication but also in various scientific experiments. The data that is being received is more and the processing time required has also become more. A significant change in the algorithms will help to optimize the processing speed. With the invention of Technologies like IoT and in technologies like Machine Learning there is a need to compress data. For example training an Artificial Neural Network requires a lot of data that should be processed and trained in small interval of time for which compression will be very helpful. There is a need to process the data faster and quicker. In this paper we present a method that reduces the data size. This method is known as Optimized Huffmans Coding. In the Huffmans coding we encode the messages so as to reduce the data and here in the optimized Huffmans coding we compress the data to a great extent which helps in various signal processing algorithms and has advantages in many applications. Here in this paper we have presented the Optimized Huffmans Coding method for Text compression. This method but has advantages over the normal Huffmans coding. This algorithm presented here says that every letter can be grouped together and encoded which not only reduces the size but also the Huffmans Tree data that is required for decoding, hence reducing the data size. This method has huge scientific applications.